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Issue No. 06 - June (2015 vol. 27)
ISSN: 1041-4347
pp: 1724-1738
Jiliang Tang , Department Computer Science and Engineering, Arizona State University, Tempe, AZ
Huiji Gao , Department Computer Science and Engineering, Arizona State University, Tempe, AZ
Atish Das Sarma , eBay Research Labs eBay Inc., San Jose, CA
Yingzhou Bi , Science Computing and Intelligent Information Processing of GuangXi Higher Education Key Laboratory, Guangxi Teachers Education University, Nanning, China
Huan Liu , Department Computer Science and Engineering, Arizona State University, Tempe, AZ
ABSTRACT
Trust plays a crucial role in helping online users collect reliable information and it has gained increasing attention from the computer science community in recent years. Traditionally, research about online trust assumes static trust relations between users. However, trust, as a social concept, evolves as people interact. Most existing studies about trust evolution are from sociologists in the physical world while little work exists in an online world. Studying online trust evolution faces unique challenges because more often than not, available data is from passive observation. In this work, we leverage social science theories to develop a methodology that enables the study of online trust evolution. In particular, we identify the differences of trust evolution study in physical and online worlds and propose a framework, eTrust, to study trust evolution using online data from passive observation in the context of product review sites by exploiting the dynamics of user preferences. We present technical details about modeling trust evolution, and perform experiments to show how the exploitation of trust evolution can help improve the performance of online applications such as trust prediction, rating prediction and ranking evolution.
INDEX TERMS
computer science, Internet, social networking (online), social sciences computing, user interfaces
CITATION

J. Tang, H. Gao, A. D. Sarma, Y. Bi and H. Liu, "Trust Evolution: Modeling and Its Applications," in IEEE Transactions on Knowledge & Data Engineering, vol. 27, no. 6, pp. 1724-1738, 2015.
doi:10.1109/TKDE.2014.2382576
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